Artificial Intelligence for COVID-19: Rapid Review

Background COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. Objective To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. Methods We performed an extensive search of the PubMed and EMBASE databases for COVID-19–related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. Results In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. Conclusions In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.

[1]  D. Chiumello,et al.  COVID-19 pneumonia: ARDS or not? , 2020, Critical Care.

[2]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[3]  Albert Hsiao,et al.  Deep Learning Localization of Pneumonia , 2020, Journal of thoracic imaging.

[4]  Leo Anthony Celi,et al.  Urban Intelligence for Pandemic Response: Viewpoint , 2020, JMIR Public Health and Surveillance.

[5]  Yan Zhao,et al.  Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. , 2020, JAMA.

[6]  Edward Santow.  Emerging from AI utopia , 2020, Science.

[7]  A. Tatem,et al.  Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study , 2020, medRxiv.

[8]  Paul Perco,et al.  Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis , 2020, International Journal of Infectious Diseases.

[9]  Dasheng Li,et al.  False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases , 2020, Korean journal of radiology.

[10]  Marlene Millen,et al.  Rapid response to COVID-19: health informatics support for outbreak management in an academic health system , 2020, J. Am. Medical Informatics Assoc..

[11]  Xian-gao Jiang,et al.  Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity , 2020 .

[12]  Geoffrey J. Gordon,et al.  Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings , 2019, Lecture Notes in Computer Science.

[13]  H. Al-Najjar,et al.  A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea. , 2020, European review for medical and pharmacological sciences.

[14]  C. Viboud,et al.  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study , 2020, The Lancet Digital Health.

[15]  L. Xia,et al.  Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. , 2020, AJR. American journal of roentgenology.

[16]  Jesse M. Ehrenfeld,et al.  The Role of Augmented Intelligence (AI) in Detecting and Preventing the Spread of Novel Coronavirus , 2020, Journal of Medical Systems.

[17]  W. Liang,et al.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions , 2020, Journal of thoracic disease.

[18]  Shayakhmetov Rim,et al.  Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches , 2020 .

[19]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[20]  Wim Naudé,et al.  Artificial intelligence vs COVID-19: limitations, constraints and pitfalls , 2020, AI & SOCIETY.

[21]  A. Phelan,et al.  Baricitinib as potential treatment for 2019-nCoV acute respiratory disease , 2020, The Lancet.

[22]  B. R. Shrestha,et al.  Managing COVID-19 in resource-limited settings: critical care considerations , 2020, Critical Care.

[23]  Mei U Wong,et al.  COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning , 2020, bioRxiv.

[24]  Bonggun Shin,et al.  Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model , 2020, Computational and Structural Biotechnology Journal.

[25]  E. Neri,et al.  Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology , 2020, La radiologia medica.

[26]  Bernhard Knapp,et al.  An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot , 2020 .

[27]  T. Mashamba-Thompson,et al.  Blockchain and Artificial Intelligence Technology for Novel Coronavirus Disease 2019 Self-Testing , 2020, Diagnostics.

[28]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[29]  Kai Zhao,et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin , 2020, Nature.

[30]  Guy Rosman,et al.  Artificial Intelligence in Anesthesiology Current Techniques , Clinical Applications , and Limitations , 2020 .

[31]  Caroline O Buckee,et al.  Using predicted imports of 2019-nCoV cases to determine locations that may not be identifying all imported cases , 2020, medRxiv.

[32]  Cdc Covid- Response Team.  Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019 — United States, February 12–March 28, 2020 , 2020 .

[33]  Sanjay Basu,et al.  Turning a blind eye: the mobilization of radiology services in resource-poor regions , 2010, Globalization and health.

[34]  Aldo A. Faisal,et al.  The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care , 2018, Nature Medicine.

[35]  Emily N. Ussery,et al.  Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019 — United States, February 12–March 28, 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[36]  K. Crawford.  The Hidden Biases in Big Data , 2013 .

[37]  Ralph Gonzales,et al.  Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic , 2020, J. Am. Medical Informatics Assoc..

[38]  T. Selden,et al.  COVID-19 And Racial/Ethnic Disparities In Health Risk, Employment, And Household Composition. , 2020, Health affairs.

[39]  Roxanne Khamsi,et al.  If a coronavirus vaccine arrives, can the world make enough? , 2020, Nature.

[40]  H. Yassine,et al.  How could artificial intelligence aid in the fight against coronavirus? , 2020, Expert review of anti-infective therapy.

[41]  Melissa D. McCradden,et al.  Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study. , 2020, CMAJ open.

[42]  Ezekiel J Emanuel,et al.  Fair Allocation of Scarce Medical Resources in the Time of Covid-19. , 2020, The New England journal of medicine.

[43]  Isaac S Kohane,et al.  Artificial Intelligence in Healthcare , 2019, Artificial Intelligence and Machine Learning for Business for Non-Engineers.

[44]  R. Evans.  European Centre for Disease Prevention and Control. , 2014, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[45]  Karel Moons,et al.  PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration , 2019, Annals of Internal Medicine.

[46]  M. Kraemer,et al.  Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel , 2020, Journal of travel medicine.

[47]  Benjamin Er,et al.  Evaluation of the Effectiveness of Surveillance and Containment Measures for the First 100 Patients with COVID-19 in Singapore — January 2–February 29, 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[48]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[49]  Robert K. Logan,et al.  The Emperor of Strong AI Has No Clothes: Limits to Artificial Intelligence , 2017, Inf..

[50]  Artem Cherkasov,et al.  Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds , 2020, Molecular informatics.

[51]  K. Kallianos,et al.  How far have we come? Artificial intelligence for chest radiograph interpretation. , 2019, Clinical radiology.

[52]  Bin Peng,et al.  In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus , 2020, Journal of Integrative Medicine.

[53]  M. Kraemer,et al.  Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study , 2020, The Lancet.

[54]  K. Cao,et al.  Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .

[55]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[56]  K. C. Santosh,et al.  AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data , 2020, Journal of Medical Systems.

[57]  Aya Sedky Adly,et al.  Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review , 2020, Journal of Medical Internet Research.

[58]  Lawrence Carin,et al.  Digital technology and COVID-19 , 2020, Nature Medicine.